Field of the Art
The disclosure relates to the field of image processing, and more particularly to the field of vector processing for large-scale satellite image processing systems.
Discussion of the State of the Art
In high-quality, high-resolution, RGB satellite ortho-mosaics, several properties are desired: (a) Seamlines between adjacent mosaic regions should be as inconspicuous as possible; (b) Color tone and saturation should be consistent on similar image content in neighboring mosaic regions; (c) Color tone and saturation should be consistent when transitioning from one side of a seamline to the other; (d) Image content in the mosaic should be tonally realistic, or otherwise plausible and aesthetically pleasing. An automated system to construct such a mosaic takes an initial collection of image strips, tonally adjusts them, constructs seamlines within the overlap area between neighboring image strips, and clips the strips to the seamlines. (In “High Value Geographic Areas”, seamlines should ride along common thin linear channels like roads, trails, and single line drainage, as well as common boundaries like city outskirts, forest boundary, mountain crests and ravines, and farm field boundaries.) The resulting mosaic regions fit together like puzzle pieces to form the mosaic.
Since automated seamline construction does not always route seamlines in a satisfactory way, what is needed is a semi-automated ability to locally edit the seamlines of a mosaic and re-clip the incident mosaic regions accordingly.
Since automated tonal matching is generally not satisfactory everywhere, what is needed is semi-automated capability for tonal matching.
What is needed is a semi-automated capability to perform global tonal adjustment, that is, adjusting gain/bias per band of a mosaic region (image strip), as a means toward tonal matching of adjacent mosaic regions. What is additionally needed is the ability for the user to select two or more mosaic regions and perform semi-automated global tonal adjustment across the selected regions as a whole, that is, adjusting the gain/bias per band across the selected regions as a whole.
In the course of tonal matching, local area features (e.g., cities) that straddle a seamline may appear tonally mismatched across a seam-line. What is needed is a semi-automated capability to perform local uniform tonal adjustment, i.e., adjusting gain/bias per band within a local constraint region of a mosaic region (image strip).
Global tonal adjustment on its own, often does not achieve sufficiently good tonal matching along seamlines between adjacent mosaic regions. This motivates the concept of a local tonal adjustment function f(x, y) that adjusts gain/bias per band per mosaic region (or per band per group of adjacent mosaic regions), that is to be applied to each pixel (x, y). If f(x, y) is given explicit definition at the boundary of a mosaic region so as to achieve good tonal matching in the vicinity of seamlines with neighboring regions, then the value of f(x,y) within the interior of the mosaic region can be smoothly interpolated from the boundary values. What is needed is both an automated and semi-automated capability to define f(x,y) at the boundary of a mosaic region, and an automated capability to smoothly interpolate f(x, y) into the interior of the mosaic region. Additionally what is needed is a semi-automated capability to insert a local tonal adjustment point P into an existing local tonal adjustment function f(x,y). Here the location and tonal adjustment values for the point are specified manually. The function f(x,y) is automatically modified to agree with the tonal adjustment values at the point (pixel) P, while continuously transitioning in the vicinity of P to the tonal adjustment values at P. Similarly, what is needed is a semi-automated capability for the removal of P and a return to the original local tonal adjustment function f(x, y).
It is undesirable for clouds to appear in a mosaic as they obscure the landscape beneath. Other adverse areas may occur in the mosaic (cloud shadow, snow patch, glint, etc.). To “correct” such an area, what is needed is a semi-automated capability that enables the user to peruse through a stack of alternate imagery covering the area, extract a corresponding patch from one of these images, and incorporate it into the current mosaic. Additionally what is needed is the capability to tonally adjust the patch so that it fits into the mosaic as seamlessly as possible.